eScience Center grants 39 new projects for 2023!
24 Jan 2023 - 2 min
We are excited to announce that we have granted 39 projects resulting from seven calls for proposals, two of which result from our partnerships with ODISSEI and the Lorentz Center. These new projects will begin in the course of 2023. We look forward to collaborating on them with researchers from across the Netherlands.
About our calls
Each year, the Netherlands eScience Center puts out a series of calls for proposals which offer a unique opportunity for researchers to enhance their work through digital methodologies. Modern day research is nearly impossible to carry out without research software. That is why we offer researchers in all scientific disciplines our expertise. As lead applicants on awarded projects, researchers collaborate with our research software engineers, who advise and develop research software. This provides researchers with the ability to focus on all other aspects of the project while leaving the software to us.
Collaborate with us:
Interested in future calls? Visit our website or subscribe to our newsletter to be the first to know about a new call for proposals opportunity.
Learn about our new projects
In 2022, we put out 7 calls for proposals, two of which we partnered with ODISSEI and the Lorentz Centre. The projects submitted through these calls were selected based on criteria that include research quality and software sustainability.
To learn about our other projects and the software that we’ve developed, please visit research-software-directory.org
We want to say a big thank you to everyone who has submitted a proposal with us.
Open eScience Call 2022
Dr. A. Sbrizzi, Universitair Medisch Centrum Utrecht
This project aims to reduce the computing time for MR-STAT data processing from 1-3 hours to within 1 minute by utilizing modern computing infrastructures and tailored algorithmic solutions.
This temporary images on the RSD pages are all low-res now, showing very blurry edges etc. Could we do an easy find & replace perhaps?
Dr. J. F. Mejias, Universiteit van Amsterdam
This project aims to develop a computational framework using artificial neural networks (ANNs) to predict the behaviour of animals trained in neuroscience tasks, leading to more efficient training protocols and improved neuroscience practices.
Dr. H. J. M. M. Mutsaerts, Amsterdam UMC
This project aims to improve the detection of blood flow patterns related to accelerated aging and cognitive decline using artificial intelligence methods. These methods will be incorporated into a software package that can be easily used by everyone to improve early disease detection and treatment evaluation.
Dr. A. H. Jonkman, Erasmus Medical Center
This project aims to develop a novel, robust and clinically meaningful data processing workflow for Electrical Impedance Tomography (EIT). This workflow will be integrated with respiratory monitoring modalities in the ICU to fully exploit the clinical benefits of EIT, promoting (inter)national research projects aimed at optimizing mechanical ventilation in the ICU.
Dr. S. Liu, Erasmus Medical Center
This project uses deep learning and interpretable machine learning tools to explore Al-learned biomarkers for cardiovascular disease progression. The goal of this project aims to gain a more comprehensive understanding of the disease for potential clinical research and applications.
Prof. dr. ir. H. A. Dijkstra, Universiteit Utrecht
This project aims to develop software using rare event algorithms to determine the probability of a collapse of the Atlantic Ocean Circulation under continuing greenhouse gas emissions and its potential climate impacts.
Dr. M. C. Bouwhuis, Nikhef
This project focuses on processing and managing the large amount of data that will be produced by the KM3NeT research infrastructure currently under construction at the bottom of the Mediterranean Sea. Using this data, we aim to study the elusive and extremely tiny particles called neutrinos to understand their origin and properties.
Dr. ir. A. Vasileiadis, Technische Universiteit Delft
This project aims to create an open-access and user-friendly Python-based analysis tool for molecular dynamics simulations. This tool will capture and plot physical properties of materials and provides intelligent visualization of the 3D diffusion environment for design and optimization in various scientific fields and applications.
Dr. ir. G. B. Koren, Universiteit Utrecht
This project aims to improve the predictions of CO2 concentrations by developing models that can accurately predict the response of CO2-exchange by vegetation in tropical regions on short and long time-scales.
Call for Collaboration in Innovative Technologies 2022
Prof. dr. S. F. Portegies Zwart, Leiden University, Leiden Observatory
This project aims to improve the accessibility, interoperability, reusability, and reproducibility of the Astrophysics Multipurpose Software Environment (AMUSE) framework by making it more FAIRe (Findable, Accessible, Interoperable, Reusable and Reproducible).
Dr. ir. B. Sanderse, CWI
This project proposes a new software framework to extend generic physics models using data-driven neural networks (NNs) that represent the effect of small scales on large scales. It does this by using differentiable programming to couple multi-scale models and NNs embedded in a learning environment, and testing it on turbulent fluid flows such as developing new differentiable wind-turbine wake models for the optimal control of wind farms.
Small-Scale Initiatives Digital Approaches to the Social Sciences
Dr. S. van Bohemen, Erasmus University
This project uses machine learning tools to trace the content and popularity of stereotypes in online pornography over a 15-year period to understand their impact on mental health and well-being, and provide new tools for analyzing large and complex video-based datasets.
Dr. A. Bagheri, Utrecht University
This project aims to optimize the ASReview open-source software’s active learning performance by allowing users to select domain-specific hyperparameters, saving time and resources, with documentation and instructions provided for less-experienced users.
Dr. A. Zhelyazkova, Erasmus University
This project aims to develop a digital tool for text processing that maps dimensions of democratic quality and assesses the precision of democratic assessments to be used for comparing the validity of information sources, analyzing democratic criteria, and released for replication and further development.
Dr. K. Thompson, Wageningen University & Research
This project aims to use simulation modeling to examine the impact of different lockdown policies on non-COVID-19 related health outcomes, specifically focusing on self-rated mental health in the Netherlands. The goal is to inform future pandemic mitigation policies and fill the gap in understanding the relationship between lockdowns and non-COVID-19-related health outcomes.
Prof. dr. C. Rieffe, University of Twente
This project aims to develop a software for smartwatches that records social behavior, movements, locations, and heart rate of children during play. This is done so in order to measure individual differences and improve understanding of how children play and experience play, particularly in children with developmental or psychiatric difficulties, by combining knowledge from psychology, psychiatry, architecture and computer science.
Dr. R. Corten, Utrecht University
The project aims to develop facilities for online experiments with participants recruited from panel surveys, specifically the LISS panel in the Netherlands. This enables researchers to run experiments with larger groups and representative samples, and to capitalize on the data already available on panel respondents to enrich experimental designs to create a unique infrastructure for ground-breaking social science research. It does this by integrating tools for online experiments with the data infrastructure of LISS and providing a well-documented public code base to be used by researchers and panel engineers for developing controlled online panel experiments.
Dr. M. Lees, University of Amsterdam
The project aims to use Agent-Based Models (ABM) to better understand the macro-level phenomenon of school segregation. We do this by calibrating an ABM on empirical, micro-level data, with the goal of improving understanding of school segregation and paving the way for other projects in the social sciences.
Prof. dr. E. Wagenmakers, University of Amsterdam
The project aims to implement a modern approach to PROCESS models in JASP, by creating two complementary graphical user interfaces (GUIs) that will help researchers apply, understand, and teach PROCESS models, overcoming the limitations of the current implementation which is in SPSS, as a catalogue of 57 models, as a frequentist methodology, and without the ability to visually represent the estimated parameters of the models.
Small-Scale Initiatives Digital Approaches to the Humanities
Dr. M. Dingemanse, Radboud University
The project aims to use computational tools to analyze linguistically diverse conversational data and make the language sciences “conversation-ready” in order to enable quantitative approaches to conversational structure and create diversity-aware language technology.
Dr. ir. M. W. Ertsen, TU Delft
This project aims to study the conditions for the long-term growth of irrigated landscapes in Mesopotamia using existing agent-based modeling in NetLogo, based on the recent scholarship that suggests that urban-based societies in this region developed within a network of increasing irrigated areas and transport connections to move surplus yields.
Dr. J. Kamps, University of Amsterdam
The project aims to improve the FAIR (Findable, Accessible, Interoperable, Reusable) publication of Woo dossiers by public bodies, which is mandatory since 1 May 2022, to meet legal requirements for government transparency in a democratic society, and make it easier to create and maintain an archive of all Woo dossiers.
Prof. dr. F. van Lieburg, VU Amsterdam
The project aims to map long-term developments in Dutch public discourse, especially in religion, by linking the national bibliography to biographical and other open-access databases, and analyzing book titles and connected meta-data. We do this to deliver a bottom-up reconstruction of trends and changes in thematization, with the focus on religion to stimulate innovations in the subdiscipline of history.
Dr. M. Rosenbaum-Feldbrügge, Radboud University
The project aims to create a data infrastructure of inhabitants of Suriname and Curaçao from 1828 to 1950 by digitizing the complete civil registry of both countries. To do this, we will use the crowdsourcing platform, HET VOLK, and explore the integration of automated handwritten text recognition and entity recognition technology to facilitate the transcription process.
Dr. M. L. Thompson, University of Groningen
The goal of the project is to use digital methods to chart shifts in the character and structure of political discourse during the era of the American Revolution.
Dr. L. Petram, Huygens Institute (KNAW)
The GLOBALISE project aims to automatically transcribe and analyze a series of historical VOC documents from the 16-18th centuries using handwriting recognition technology and NLP techniques to improve accuracy and enable named entity recognition and event detection.
Dr. L. Costiner, Utrecht University
This project is the first to use computational techniques to study artistic practice by analyzing reproductions of Raphael’s paintings, providing new insights into the artist’s working methods and pioneering new digital approaches for the study of artistic practice in art history.
Small-Scale Initiatives Machine Learning
Dr. J. Doorduin, Radboud University Medical Center
This project aims to develop new software based on machine learning techniques that will improve speed and performance of muscle ultrasound, a screening tool for neuromuscular diseases, in order to accelerate its use and value in research and the clinical setting.
Dr. D. Vu, Westerdijk Fungal Biodiversity Institute, KNAW
The goal is to develop an open-source deep learning application to efficiently and accurately identify microbial taxonomic profiles in environmental samples to trace microbial biodiversity changes in the Netherlands and other parts of the world.
Dr. R. Saathof, TU Delft
The goal is to propose using a single star Scintillation Detection and Ranging (SCIDAR) instrument that utilizes machine learning for reliable signal processing of received images of a star. This is done so in order to estimate the turbulence strength for optical links through the atmosphere at rural or urban sites in the growing research field of optical satellite communications.
Dr. F. Anselmucci, University of Twente
This project aims to use machine learning to improve the identification capability of 3D-image processing for soil characterization. We will do this by training classifiers with high-fidelity, physics-based simulation data, in order to better observe water transport and how roots and soil react to water cycles during the growth of young maize, as the health of soil is as important as the health of humans.
Dr. ir. B. Ensing, University of Amsterdam
The project aims to use a new class of machine learning techniques called diffusion models to generate new interesting molecules.
Open ODISSEI eScience Call 2022
Dr. G. J. Brandsma, Radboud University
EU rules touch upon virtually all aspects of human and economic life, but little is known about their strictness. The goal of the project is to conduct a large-scale content analysis of all European directives and regulations ever adopted to determine if EU law is mostly strict and detailed or generally lacking bite. Furthermore, we will explore if this varies between issue areas, who is affected by EU law, and if it has changed over time, as little is known about EU law strictness, and currently, no research project has ever analyzed the contents of the full body of EU law.
Prof. dr. J. M. Tybur, VU Amsterdam
The project aims to discover when and why people systematically produce disgust vocalizations by testing whether there are acoustically different kinds of disgust vocalizations associated with different contextual features.
Prof. dr. J. Tolsma, University of Groningen
The project aims to understand how, when and where residential segregation is related to political polarization in the Netherlands, by using geographic data at a fine granular level mapping the whole Netherlands.
Dr. B. Hofstra, Radboud University
The goal of this project is to study how gender and ethnic inequality among Dutch PhD recipients affects labor market outcomes, by using big data of nearly all Dutch PhDs from 1990-2021, as this is an issue that affects the academic labor market but also has an impact on individual scientists and the representation of talented academics.
Prof. dr. R. van de Schoot, Utrecht University
The project aims to develop a one-click-deployment option for running large-scale simulation studies in the cloud using the open-source software ASReview in order to save computation time.
eScience Center – Lorentz Competition 2022
Prof. dr. I. Arts, Maastricht University
The project aims to create a versatile, open-source technology platform called the Dietary Digital twin (DDtwin) that combines biology-based and data-driven models connected to real-life data to enable next-gen precision nutrition.
Dr. ir. F.C. Vossepoel, TU Delft
Digital twins are digital representations of human-environment-technology interactions that can be used for decision support and scenario evaluation, and are being developed globally to secure the resilience of delta regions under conditions of a changing climate, in which a prototype and perspective on its requirements is being developed through a five-day workshop with stakeholders and researchers.
Learn more about this project.